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added number of incubation days as property of the virus and adapted tests

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This commit is contained in:
Luis Aleixo 2022-10-05 14:28:53 +02:00
parent 9032a8815d
commit 7a9401a6fa
2 changed files with 34 additions and 22 deletions
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14
caimira/models.py
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@ -439,6 +439,9 @@ class Virus:
#: Pre-populated examples of Viruses.
types: typing.ClassVar[typing.Dict[str, "Virus"]]
#: Number of incubation days
infectiousness_days: int = 14
def halflife(self, humidity: _VectorisedFloat, inside_temp: _VectorisedFloat) -> _VectorisedFloat:
# Biological decay (inactivation of the virus in air) - virus
# dependent and function of humidity
@ -927,16 +930,16 @@ class Cases:
#: Number of new cases confidence level
ascertainment_bias: int = 0
def probability_random_individual(self) -> _VectorisedFloat:
def probability_random_individual(self, virus: Virus) -> _VectorisedFloat:
"""Probability that a randomly selected individual in a focal population is infected."""
return self.geographic_cases*self.ascertainment_bias/self.geographic_population
return self.geographic_cases*virus.infectiousness_days*self.ascertainment_bias/self.geographic_population
def probability_meet_infected_person(self, event_population: int, n_infected: int) -> _VectorisedFloat:
def probability_meet_infected_person(self, virus: Virus, event_population: int, n_infected: int) -> _VectorisedFloat:
"""
Probability to meet n_infected persons in an event.
From https://doi.org/10.1038/s41562-020-01000-9.
"""
return sct.binom.pmf(n_infected, event_population, self.probability_random_individual())
return sct.binom.pmf(n_infected, event_population, self.probability_random_individual(virus))
@dataclass(frozen=True)
@ -1481,7 +1484,8 @@ class ExposureModel:
)
prob_exposed_occupant = exposure_model.infection_probability().mean() / 100
# By means of a Binomial Distribution
sum_probability += (prob_exposed_occupant)*self.geographical_data.probability_meet_infected_person(self.exposed.number, num_infected)
sum_probability += (prob_exposed_occupant *
self.geographical_data.probability_meet_infected_person(self.concentration_model.infected.virus, self.exposed.number, num_infected))
return sum_probability * 100
else:
return 0

42
caimira/tests/models/test_exposure_model.py
View file

@ -234,43 +234,51 @@ def test_infectious_dose_vectorisation(sr_model):
@pytest.mark.parametrize(
"pop, cases, AB, prob_random_individual", [
[100_000, 67, 5, 0.00335],
[200_000, 121, 5, 0.003025],
[np.array([100_000, 200_000]), 67, 10, np.array([0.0067, 0.00335])],
[150_000, np.array([67, 121]), 2, np.array([0.00089333, 0.00161333])],
[np.array([100_000, 200_000]), np.array([67, 121]), 5, np.array([0.00335, 0.003025])]
"pop, cases, infectiousness_days, AB, prob_random_individual", [
[100_000, 68, 7, 5, 0.02345],
[200_000, 121, np.array([7, 14]), 5, np.array([0.021175, 0.042350])],
[np.array([100_000, 200_000]), 68, 14, 10, np.array([0.0952, 0.0476])],
[150_000, np.array([68, 121]), 14, 2, np.array([0.012693, 0.022587])],
[np.array([100_000, 200_000]), np.array([68, 121]), 7, 5, np.array([0.023450, 0.021175])]
]
)
def test_probability_random_individual(pop, cases, AB, prob_random_individual):
model = models.Cases(geographic_population=pop, geographic_cases=cases,
def test_probability_random_individual(pop, cases, infectiousness_days, AB, prob_random_individual):
cases = models.Cases(geographic_population=pop, geographic_cases=cases,
ascertainment_bias=AB)
virus=models.SARSCoV2(
viral_load_in_sputum=1e9,
infectious_dose=50.,
viable_to_RNA_ratio = 0.5,
transmissibility_factor=1,
infectiousness_days=infectiousness_days,
)
np.testing.assert_allclose(
model.probability_random_individual(), prob_random_individual, rtol=0.05
cases.probability_random_individual(virus), prob_random_individual, rtol=0.05
)
@pytest.mark.parametrize(
"pop, cases, AB, exposed, infected, prob_meet_infected_person", [
[100_000, 67, 5, 10, 2, 0.00049],
[200_000, 121, 5, 10, 1, 0.02944],
[np.array([100_000, 200_000]), 67, 10, 15, 2, np.array([0.00432, 0.00113])],
[150_000, np.array([67, 121]), 2, np.array([10, 15]), np.array([1, 2]), np.array([0.00886, 0.00027])],
[100_000, 68, 5, 10, 1, 0.306889],
[200_000, 121, 5, 10, 1, 0.286890],
[np.array([100_000, 200_000]), 68, 10, 15, 2, np.array([0.259207, 0.126199])],
[150_000, np.array([68, 121]), 2, np.array([10, 15]), np.array([1, 2]), np.array([0.113147, 0.039803])],
]
)
def test_prob_meet_infected_person(pop, cases, AB, exposed, infected, prob_meet_infected_person):
model = models.Cases(geographic_population=pop, geographic_cases=cases,
cases = models.Cases(geographic_population=pop, geographic_cases=cases,
ascertainment_bias=AB)
np.testing.assert_allclose(model.probability_meet_infected_person(exposed, infected),
virus = models.Virus.types['SARS_CoV_2']
np.testing.assert_allclose(cases.probability_meet_infected_person(virus, exposed, infected),
prob_meet_infected_person, rtol=0.05)
@pytest.mark.parametrize(
"population, cm, pop, cases, AB, probabilistic_exposure_probability",[
[populations[1], known_concentrations(lambda t: 36.),
100000, 68, 5, 2.24124],
100000, 68, 5, 27.537276],
[populations[0], known_concentrations(lambda t: 36.),
100000, 68, 5, 1.875652],
100000, 68, 5, 23.540145],
])
def test_probabilistic_exposure_probability(population, cm,
pop, AB, cases, probabilistic_exposure_probability):